Lin Siyun, Ma Zhuangxuan, Yao Yuanshan, Huang Hou, Chen Wufei, Tang Dongfang, Gao Wen
Huadong Hospital, Fudan University, Department of Thoracic Surgery, Shanghai, China.
Shanghai Key Laboratory of Clinical Geriatric Medicine, Shanghai, China.
Diagn Interv Radiol. 2025 Mar 3;31(2):130-140. doi: 10.4274/dir.2024.242972. Epub 2025 Jan 16.
Patients with advanced non-small cell lung cancer (NSCLC) have varying responses to immunotherapy, but there are no reliable, accepted biomarkers to accurately predict its therapeutic efficacy. The present study aimed to construct individualized models through automatic machine learning (autoML) to predict the efficacy of immunotherapy in patients with inoperable advanced NSCLC.
A total of 63 eligible participants were included and randomized into training and validation groups. Radiomics features were extracted from the volumes of interest of the tumor circled in the preprocessed computed tomography (CT) images. Golden feature, clinical, radiomics, and fusion models were generated using a combination of various algorithms through autoML. The models were evaluated using a multi-class receiver operating characteristic curve.
In total, 1,219 radiomics features were extracted from regions of interest. The ensemble algorithm demonstrated superior performance in model construction. In the training cohort, the fusion model exhibited the highest accuracy at 0.84, with an area under the curve (AUC) of 0.89-0.98. In the validation cohort, the radiomics model had the highest accuracy at 0.89, with an AUC of 0.98-1.00; its prediction performance in the partial response subgroup outperformed that in both the clinical and radiomics models. Patients with low rad scores achieved improved progression-free survival (PFS); (median PFS 16.2 vs. 13.4, = 0.009).
autoML accurately and robustly predicted the short-term outcomes of patients with inoperable NSCLC treated with immune checkpoint inhibitor immunotherapy by constructing CT-based radiomics models, confirming it as a powerful tool to assist in the individualized management of patients with advanced NSCLC.
This article highlights that autoML promotes the accuracy and efficiency of feature selection and model construction. The radiomics model generated by autoML predicted the efficacy of immunotherapy in patients with advanced NSCLC effectively. This may provide a rapid and non-invasive method for making personalized clinical decisions.
晚期非小细胞肺癌（NSCLC）患者对免疫疗法的反应各不相同，但尚无可靠、公认的生物标志物来准确预测其治疗效果。本研究旨在通过自动机器学习（autoML）构建个体化模型，以预测不可手术的晚期NSCLC患者免疫疗法的疗效。
共纳入63名符合条件的参与者，并随机分为训练组和验证组。从预处理计算机断层扫描（CT）图像中圈出的肿瘤感兴趣体积中提取影像组学特征。通过autoML使用各种算法的组合生成黄金特征、临床、影像组学和融合模型。使用多类受试者工作特征曲线对模型进行评估。
总共从感兴趣区域提取了1219个影像组学特征。集成算法在模型构建中表现出卓越性能。在训练队列中，融合模型表现出最高准确性，为0.84，曲线下面积（AUC）为0.89 - 0.98。在验证队列中，影像组学模型准确性最高，为0.89，AUC为0.98 - 1.00；其在部分缓解亚组中的预测性能优于临床和影像组学模型。低影像组学评分的患者无进展生存期（PFS）得到改善；（中位PFS 16.2对13.4，P = 0.009）。
autoML通过构建基于CT的影像组学模型，准确且稳健地预测了接受免疫检查点抑制剂免疫治疗的不可手术NSCLC患者的短期结局，证实其为协助晚期NSCLC患者个体化管理的有力工具。
本文强调autoML提高了特征选择和模型构建的准确性和效率。autoML生成的影像组学模型有效预测了晚期NSCLC患者免疫疗法的疗效。这可能为做出个性化临床决策提供一种快速且无创的方法。
